PD Differential Gene Expression - Gene enrichment analysis
Hardy project dataset
Hardy project dataset
Corrected identification script
Guillermo Rocamora Pérez
UCL - Version 1.304 December 2024
# Load libraries
library(DESeq2)
library(edgeR)
library(limma)
library(variancePartition)
library(foreach)
library(clusterProfiler)
library(org.Hs.eg.db)
library(ggh4x)
library(magrittr)
library(enrichplot)
library(patchwork)
library(fgsea)
library(doParallel)
library(tidyverse)
# Define main path
# main_path <- file.path("/home/grocamora/RytenLab-Research/18-DGE_limma_dream")# here::here()
main_path <- file.path("/home/jbrenton/ASAP_Hardy_post_align_files/Final_Dataset/Hardy_Limma_Analysis_Final_version_only")
# Load helper functions
# source(file.path(main_path, "R/hf_additional.R"))
source(file.path(main_path, "R/hf_graph_and_themes.R"))
# source(file.path(main_path, "R/hf_intron_categories.R"))
source(file.path(main_path, "R/hf_reports.R"))
# Set options
options(dplyr.summarise.inform = FALSE)
options(lifecycle_verbosity = "warning")
knitr::opts_chunk$set(echo = T, warning = F, message = F,
out.width="100%", fig.align = "center", dpi = 150,
fig.width = 7.2, fig.height = 7.2)1 Background
Methodoly and documentation: TBD
This report includes the gene enrichment analysis of the differentially expressed genes found in the previous report. We decided to focus on three different databases: GO, KEGG and REACTOME.
First, we need to load the results from the previous report and set the parameters that will dictate the results:
# Paths
# main_path <- "/home/grocamora/RytenLab-Research/18-DGE_limma_dream"
# results_path <- file.path(main_path, "results/Hardy_NewCovs")
results_path <- file.path(main_path, "results")
hardy_dge_results_path <- file.path(results_path, "dge_variables/")
deg_all_output_path <- file.path(hardy_dge_results_path, "PD_Collapsed_dge_all.rds")
gene_enrichment_results_path <- file.path(results_path, "gene_enrichment_variables/")
dir.create(gene_enrichment_results_path, showWarnings = F, recursive = T)
formula_res_merge_path <- file.path(gene_enrichment_results_path, "pd_collapsed_formula_GO.rds")
formula_res_merge_red_path <- file.path(gene_enrichment_results_path, "pd_collapsed_formula_GO_red.rds")
formula_res_KEGG_path <- file.path(gene_enrichment_results_path, "pd_collapsed_formula_KEGG.rds")
formula_res_REACTOME_path <- file.path(gene_enrichment_results_path, "pd_collapsed_formula_REACTOME.rds")
# Load data
deg_all_output <- readRDS(deg_all_output_path)
# Report parameters
count_threshold <- 2
go_reduce_threshold <- 0.5We will employ clusterProfiler for the enrichment analysis. We also need to load the gene information - i.e. gene symbol, ENTREZID and ENSEMBL ID.
# Load gene info
uniKeys <- AnnotationDbi::keys(org.Hs.eg.db, keytype = "ENSEMBL")
cols <- c("SYMBOL", "ENTREZID", "ENSEMBL")
cross_comp_list <- AnnotationDbi::select(org.Hs.eg.db, keys = uniKeys, columns = cols, keytype = "ENSEMBL")
# Remove duplicate symbols
non_ducplicates_idx <- which(duplicated(cross_comp_list$SYMBOL) == F)
cross_comp_list <- cross_comp_list[non_ducplicates_idx, ]From the results previously loaded, we generate the clusters of data. Two different grouping scenearios are going to be considered: collapsed by brain region and brain region specific.
# Generate the cluster data combined
cluster_data <- deg_all_output %>%
# dplyr::filter(method == "limma") %>%
# dplyr::select(-method) %>%
dplyr::mutate(test = stringr::str_replace_all(test, "-", " vs ")) %>%
dplyr::mutate(test = factor(test, levels = c("PD vs Control"))) %>%
dplyr::left_join(cross_comp_list, by = c("gene_id" = "ENSEMBL"), relationship = "many-to-many") %>%
dplyr::select(ENTREZID, SYMBOL, logFC, adj.P.Val, tissue, sig_reg, test) %>%
dplyr::filter(!is.na(ENTREZID))
cluster_data_sig <- cluster_data %>%
dplyr::filter(sig_reg != "Unchanged") %>%
dplyr::distinct(ENTREZID, tissue, test, .keep_all = T)
universe_genes <- cluster_data$ENTREZID %>% unique()Changelog
v1.3
Updated to R v4.3.0
The GO reduction is now consistent with the Wood dataset.
v1.2
- Fixed covariate identification mistake.
v1.1
Modified plots to better represent the pathways.
Refactoring code to better handle the different scenarios.
v1.0
Initial report.
Added GO term reduction.
1.1 Helper functions
generatePlotDataframe <- function(formula_res){
if("parent_term" %in% colnames(formula_res@compareClusterResult)){
description_names <- formula_res@compareClusterResult %>%
dplyr::distinct(Description, parent_term) %>%
dplyr::group_by(parent_term) %>%
dplyr::count() %>%
dplyr::mutate(mod_description = stringr::str_to_title(paste0(parent_term, " (", n, ")"))) %>%
dplyr::mutate(mod_description = paste0(strwrap(mod_description, width = 50, prefix = "\n", initial = ""),
collapse = "")) %>%
dplyr::select(parent_term, n, mod_description)
plot_df <- formula_res@compareClusterResult %>%
dplyr::group_by(test, tissue, sig_reg, parent_term) %>%
dplyr::filter(p.adjust == min(p.adjust)) %>%
dplyr::filter(parent_sim_score == max(parent_sim_score)) %>%
dplyr::ungroup() %>%
dplyr::left_join(description_names, by = "parent_term") %>%
dplyr::mutate(Description = mod_description) %>%
# tidyr::separate(GeneRatio, into = c("gr1", "gr2"), convert = T) %>%
# dplyr::mutate(geneRatio = gr1/gr2) %>%
dplyr::group_by(Description) %>%
dplyr::mutate(min.padj = min(p.adjust)) %>%
dplyr::ungroup() %>%
dplyr::arrange(desc(n), test, tissue, p.adjust) %>%
dplyr::mutate(Description = factor(Description, levels = unique(Description)),
sig_reg = factor(sig_reg, levels = c("Significantly Downregulated", "Significantly Upregulated")),
test = factor(test, levels = c("PD vs Control")),
tissue = factor(tissue, levels = c("ACG", "IPL", "MFG", "MTG")))
}else{
plot_df <- formula_res@compareClusterResult %>%
dplyr::rowwise() %>%
dplyr::mutate(Description = stringr::str_to_title(Description),
Description = paste0(strwrap(Description, width = 50, prefix = "\n", initial = ""),
collapse = "")) %>%
dplyr::group_by(Description) %>%
dplyr::mutate(n = n(),
min.padj = min(p.adjust)) %>%
dplyr::ungroup() %>%
dplyr::arrange(desc(n), min.padj, test, tissue, p.adjust) %>%
dplyr::mutate(Description = factor(Description, levels = unique(Description)),
sig_reg = factor(sig_reg, levels = c("Significantly Downregulated", "Significantly Upregulated")),
test = factor(test, levels = c("PD vs Control")),
tissue = factor(tissue, levels = c("ACG", "IPL", "MFG", "MTG")))
}
}
drawPathwaysGO <- function(df_plot, x = "test", x_expansion = c(0.5, 0.5), drop_facet = T, drop_x = F){
plot <- plot_df %>%
ggplot(aes(x = .data[[x]], y = Description, fill = -log10(p.adjust))) +
geom_tile(color = "black", width = 1, show.legend = T) +
geom_hline(yintercept = seq(0.5, length(plot_df$Description)), color="black", linewidth = 0.2) +
geom_vline(xintercept = seq(0.5, length(plot_df[, x, T])+1), color="black", linewidth = 0.2) +
scale_y_discrete(limits = rev, expand = expansion()) +
scale_x_discrete(drop = drop_x, expand = expansion(add = x_expansion)) +
scale_fill_viridis_c(name = "-log"[1][0]~"(FDR)", breaks = c(0, 2, 4, 6, 8, 10),
na.value = "white", option = "cividis") +
coord_equal() +
labs(x = "", y = "GO Term", fill = "hola") +
custom_gg_theme +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
strip.text.x = element_text(size = 9, face = "plain"),
panel.background = element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid = element_blank())
# Facet the plots based on what they have on the X-axis
if(x == "test"){
plot <- plot +
ggh4x::facet_nested(. ~ sig_reg, drop = drop_facet,
labeller = labeller(sig_reg = c("Significantly Upregulated" = "Up-Reg.",
"Significantly Downregulated" = "Down-Reg.")))
}else if(x == "tissue"){
plot <- plot +
ggh4x::facet_nested(. ~ sig_reg + test, drop = drop_facet,
labeller = labeller(sig_reg = c("Significantly Upregulated" = "Up-Reg.",
"Significantly Downregulated" = "Down-Reg.")))
}
return(plot)
}
# New function to go with updated rrvgo - problem mainly with reduce sim function - both score parameter and parent_sim_score are changed
# scores is now a character vector choice of either "size" or "uniqueness" - uniqueness is default
# parent sim score is now divided into term uniqueness and term dissimilaity or something
updated_go_reduce<-function (pathway_df, orgdb = "org.Hs.eg.db", threshold = 0.7,
scores = NULL, measure = "Wang")
{
if (!measure %in% c("Resnik", "Lin", "Rel", "Jiang", "Wang")) {
stop("Chosen measure is not one of the recognised measures, c(\"Resnik\", \"Lin\", \"Rel\", \"Jiang\", \"Wang\").")
}
if (measure == "Wang") {
computeIC <- FALSE
}
else {
computeIC <- TRUE
}
ont <- pathway_df %>% .[["go_type"]] %>% unique()
if (any(!ont %in% c("BP", "CC", "MF"))) {
stop("Column go_type does not contain the recognised sub-ontologies, c(\"BP\", \"CC\", \"MF\")")
}
go_similarity <- setNames(object = vector(mode = "list",
length = length(ont)), nm = ont)
for (i in 1:length(ont)) {
print(stringr::str_c("Reducing sub-ontology: ", ont[i]))
hsGO <- GOSemSim::godata(OrgDb = orgdb, ont = ont[i],
computeIC = computeIC)
terms <- pathway_df %>% dplyr::filter(.data$go_type ==
ont[i]) %>% .[["go_id"]] %>% unique()
sim <- GOSemSim::mgoSim(GO1 = terms, GO2 = terms, semData = hsGO,
measure = measure, combine = NULL)
go_similarity[[i]] <- rrvgo::reduceSimMatrix(simMatrix = sim,
threshold = threshold, orgdb = orgdb) %>%
tibble::as_tibble() %>% dplyr::rename(parent_id = .data$parent,
parent_term = .data$parentTerm, parent_sim_score = .data$termUniqueness)
}
go_sim_df <- go_similarity %>% qdapTools::list_df2df(col1 = "go_type")
pathway_go_sim_df <- pathway_df %>%
dplyr::inner_join(go_sim_df %>%
dplyr::select(.data$go_type, go_id = .data$go, contains("parent")),
by = c("go_type", "go_id")) %>%
dplyr::arrange(.data$go_type, .data$parent_id, -.data$parent_sim_score)
return(pathway_go_sim_df)
}2 GO enrichment analysis
Two different visualizations are presented: i) GO terms as extracted from enrichGO() and ii) GO terms collapsed by similarity from Regina H. Reynolds’ function go_reduce(), from her package rutils.
If several GO terms are combined into one, the GeneRatio and adjusted p-value shown in the plots correspond to the minimum adjusted p-value of the collapsed terms. The number in brackets next to the Y-axis labels represent the number of GO terms collapsed.
if(!file_test("-f", formula_res_merge_path, formula_res_merge_red_path)){
formula_res_mf <- clusterProfiler::compareCluster(
ENTREZID ~ sig_reg + tissue + test,
data = cluster_data_sig, fun = enrichGO,
OrgDb = org.Hs.eg.db, ont = "MF",
universe = universe_genes,
readable = T) %>%
dplyr::mutate(go_type = "MF", go_id = ID)
formula_res_bp <- clusterProfiler::compareCluster(
ENTREZID ~ sig_reg + tissue + test,
data = cluster_data_sig, fun = enrichGO,
OrgDb = org.Hs.eg.db, ont = "BP",
universe = universe_genes,
readable = T) %>%
dplyr::mutate(go_type = "BP", go_id = ID)
formula_res_cc <- clusterProfiler::compareCluster(
ENTREZID ~ sig_reg + tissue + test,
data = cluster_data_sig, fun = enrichGO,
OrgDb = org.Hs.eg.db, ont = "CC",
universe = universe_genes,
readable = T) %>%
dplyr::mutate(go_type = "CC", go_id = ID)
# Reduce the GO terms
formula_res_mf_red <- formula_res_mf
formula_res_bp_red <- formula_res_bp
formula_res_cc_red <- formula_res_cc
formula_res_mf_red@compareClusterResult %<>% updated_go_reduce(threshold = go_reduce_threshold)
formula_res_bp_red@compareClusterResult %<>% updated_go_reduce(threshold = go_reduce_threshold)
formula_res_cc_red@compareClusterResult %<>% updated_go_reduce(threshold = go_reduce_threshold)
# Filter pathways
formula_res_mf@compareClusterResult %<>% dplyr::filter(Count >= count_threshold)
formula_res_bp@compareClusterResult %<>% dplyr::filter(Count >= count_threshold)
formula_res_cc@compareClusterResult %<>% dplyr::filter(Count >= count_threshold)
formula_res_mf_red@compareClusterResult %<>% dplyr::filter(Count >= count_threshold)
formula_res_bp_red@compareClusterResult %<>% dplyr::filter(Count >= count_threshold)
formula_res_cc_red@compareClusterResult %<>% dplyr::filter(Count >= count_threshold)
# Merge ontologies
formula_res_merge <- clusterProfiler::merge_result(list(mf = formula_res_mf,
bp = formula_res_bp,
cc = formula_res_cc))
formula_res_merge_red <- clusterProfiler::merge_result(list(mf = formula_res_mf_red,
bp = formula_res_bp_red,
cc = formula_res_cc_red))
# Rename ontologies
names(formula_res_merge@compareClusterResult)[1] <- "Ontology"
names(formula_res_merge_red@compareClusterResult)[1] <- "Ontology"
# Save results
formula_res_merge %>% saveRDS(formula_res_merge_path)
formula_res_merge_red %>% saveRDS(formula_res_merge_red_path)
}else{
formula_res_merge <- readRDS(formula_res_merge_path)
formula_res_merge_red <- readRDS(formula_res_merge_red_path)
}2.1 Collapsed brain regions
Both
Expand to see results for default GO terms.
formula_res_merge_both <- formula_res_merge %>% dplyr::filter(tissue == "Collapse")
plot_df <- generatePlotDataframe(formula_res_merge_both)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df, drop_facet = F)
}else{
print("No pathways found in this scenario.")
}
Expand to see results for reduced GO terms.
formula_res_merge_red_both <- formula_res_merge_red %>% dplyr::filter(tissue == "Collapse")
plot_df <- generatePlotDataframe(formula_res_merge_red_both)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df, drop_facet = F)
}else{
print("No pathways found in this scenario.")
}
Upregulated
Expand to see results for default GO terms.
formula_res_merge_up <- formula_res_merge %>% dplyr::filter(grepl("Upregulated", sig_reg), tissue == "Collapse")
plot_df <- generatePlotDataframe(formula_res_merge_up)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df)
}else{
print("No pathways found in this scenario.")
}## [1] "No pathways found in this scenario."
Expand to see results for reduced GO terms.
formula_res_merge_red_up <- formula_res_merge_red %>% dplyr::filter(grepl("Upregulated", sig_reg), tissue == "Collapse")
plot_df <- generatePlotDataframe(formula_res_merge_red_up)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df)
}else{
print("No pathways found in this scenario.")
}## [1] "No pathways found in this scenario."
Downregulated
Expand to see results for default GO terms.
formula_res_merge_down <- formula_res_merge %>% dplyr::filter(grepl("Downregulated", sig_reg), tissue == "Collapse")
plot_df <- generatePlotDataframe(formula_res_merge_down)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df)
}else{
print("No pathways found in this scenario.")
}
Expand to see results for reduced GO terms.
formula_res_merge_red_down <- formula_res_merge_red %>% dplyr::filter(grepl("Downregulated", sig_reg), tissue == "Collapse")
plot_df <- generatePlotDataframe(formula_res_merge_red_down)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df)
}else{
print("No pathways found in this scenario.")
}
2.2 By brain regions
Both
Expand to see results for default GO terms.
formula_res_merge_both <- formula_res_merge %>% dplyr::filter(tissue != "Collapse")
plot_df <- generatePlotDataframe(formula_res_merge_both)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df, x = "tissue", drop_facet = T)
}else{
print("No pathways found in this scenario.")
}
Expand to see results for reduced GO terms.
formula_res_merge_red_both <- formula_res_merge_red %>% dplyr::filter(tissue != "Collapse")
plot_df <- generatePlotDataframe(formula_res_merge_red_both)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df, x = "tissue", drop_facet = T) + theme(axis.text.y = element_text(size=12))
}else{
print("No pathways found in this scenario.")
}
Upregulated
Expand to see results for default GO terms.
formula_res_merge_up <- formula_res_merge %>% dplyr::filter(grepl("Upregulated", sig_reg), tissue != "Collapse")
plot_df <- generatePlotDataframe(formula_res_merge_up)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df, x = "tissue")
}else{
print("No pathways found in this scenario.")
}
Expand to see results for reduced GO terms.
formula_res_merge_red_up <- formula_res_merge_red %>% dplyr::filter(grepl("Upregulated", sig_reg), tissue != "Collapse")
plot_df <- generatePlotDataframe(formula_res_merge_red_up)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df, x = "tissue")
}else{
print("No pathways found in this scenario.")
}
Downregulated
Expand to see results for default GO terms.
formula_res_merge_down <- formula_res_merge %>% dplyr::filter(grepl("Downregulated", sig_reg), tissue != "Collapse")
plot_df <- generatePlotDataframe(formula_res_merge_down)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df, x = "tissue")
}else{
print("No pathways found in this scenario.")
}
Expand to see results for reduced GO terms.
formula_res_merge_red_down <- formula_res_merge_red %>% dplyr::filter(grepl("Downregulated", sig_reg), tissue != "Collapse")
plot_df <- generatePlotDataframe(formula_res_merge_red_down)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df, x = "tissue")
}else{
print("No pathways found in this scenario.")
}
2.3 Merge to output to csv
go_merge_no_red<-rbind(plot_df_collapse, plot_df_indv_bas) %>% arrange(p.adjust)
write_csv(x = go_merge_no_red, file = file.path(gene_enrichment_results_path, "PD_collapsed_Sig_GO_terms_no_reduction.csv"))
go_merge_red<-rbind(plot_df_collapse_red,plot_df_indv_bas_red) %>% arrange(p.adjust)
write_csv(x = go_merge_red, file = file.path(gene_enrichment_results_path, "PD_collapsed_Sig_GO_terms_merged_with_term_reduction.csv"))3 KEGG enrichment analysis
if(!file_test("-f", formula_res_KEGG_path)){
formula_res_KEGG <- clusterProfiler::compareCluster(
ENTREZID ~ sig_reg + tissue + test,
data = cluster_data_sig, fun = enrichKEGG,
organism = "hsa",
universe = universe_genes) %>%
clusterProfiler::setReadable(OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
formula_res_KEGG@compareClusterResult %<>% dplyr::filter(Count >= count_threshold)
# Save results
formula_res_KEGG %>% saveRDS(formula_res_KEGG_path)
}else{
formula_res_KEGG <- readRDS(formula_res_KEGG_path)
}3.1 Collapsed brain regions
Both
formula_res_KEGG_both <- formula_res_KEGG %>% dplyr::filter(tissue == "Collapse")
plot_df <- generatePlotDataframe(formula_res_KEGG_both)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df, drop_facet = F)
}else{
print("No pathways found in this scenario.")
}## [1] "No pathways found in this scenario."Upregulated
formula_res_KEGG_up <- formula_res_KEGG %>% dplyr::filter(grepl("Upregulated", sig_reg), tissue == "Collapse")
plot_df <- generatePlotDataframe(formula_res_KEGG_up)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df)
}else{
print("No pathways found in this scenario.")
}## [1] "No pathways found in this scenario."Downregulated
formula_res_KEGG_down <- formula_res_KEGG %>% dplyr::filter(grepl("Downregulated", sig_reg), tissue == "Collapse")
plot_df <- generatePlotDataframe(formula_res_KEGG_down)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df)
}else{
print("No pathways found in this scenario.")
}## [1] "No pathways found in this scenario."3.2 By brain regions
Both
formula_res_KEGG_both <- formula_res_KEGG %>% dplyr::filter(tissue != "Collapse")
plot_df <- generatePlotDataframe(formula_res_KEGG_both)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df, x = "tissue", drop_facet = F)
}else{
print("No pathways found in this scenario.")
}
Upregulated
formula_res_KEGG_up <- formula_res_KEGG %>% dplyr::filter(grepl("Upregulated", sig_reg), tissue != "Collapse")
plot_df <- generatePlotDataframe(formula_res_KEGG_up)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df, x = "tissue")
}else{
print("No pathways found in this scenario.")
}## [1] "No pathways found in this scenario."
4 REACTOME enrichment analysis
if(!file_test("-f", formula_res_REACTOME_path)){
formula_res_REACTOME <- clusterProfiler::compareCluster(
ENTREZID ~ sig_reg + tissue + test,
data = cluster_data_sig, fun = "enrichPathway",
readable = T)
formula_res_REACTOME@compareClusterResult %<>% dplyr::filter(Count >= count_threshold)
# Save results
formula_res_REACTOME %>% saveRDS(formula_res_REACTOME_path)
}else{
formula_res_REACTOME <- readRDS(formula_res_REACTOME_path)
}4.1 Collapsed brain regions
Both
formula_res_REACTOME_both <- formula_res_REACTOME %>% dplyr::filter(tissue == "Collapse")
plot_df <- generatePlotDataframe(formula_res_REACTOME_both)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df, drop_facet = F)
}else{
print("No pathways found in this scenario.")
}
Upregulated
formula_res_REACTOME_up <- formula_res_REACTOME %>% dplyr::filter(grepl("Upregulated", sig_reg), tissue == "Collapse")
plot_df <- generatePlotDataframe(formula_res_REACTOME_up)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df)
}else{
print("No pathways found in this scenario.")
}## [1] "No pathways found in this scenario."
4.2 By brain regions
Both
formula_res_REACTOME_both <- formula_res_REACTOME %>% dplyr::filter(tissue != "Collapse")
plot_df <- generatePlotDataframe(formula_res_REACTOME_both)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df, x = "tissue", drop_facet = F)
}else{
print("No pathways found in this scenario.")
}
Upregulated
formula_res_REACTOME_up <- formula_res_REACTOME %>% dplyr::filter(grepl("Upregulated", sig_reg), tissue != "Collapse")
plot_df <- generatePlotDataframe(formula_res_REACTOME_up)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df, x = "tissue")
}else{
print("No pathways found in this scenario.")
}## [1] "No pathways found in this scenario."Downregulated
formula_res_REACTOME_down <- formula_res_REACTOME %>% dplyr::filter(grepl("Downregulated", sig_reg), tissue != "Collapse")
plot_df <- generatePlotDataframe(formula_res_REACTOME_down)
if(nrow(plot_df) > 0){
drawPathwaysGO(plot_df, x = "tissue")
}else{
print("No pathways found in this scenario.")
}
5 Session Info
Click to Show
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.3.0 (2023-04-21)
## os Ubuntu 20.04.6 LTS
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_GB.UTF-8
## ctype en_GB.UTF-8
## tz Etc/UTC
## date 2024-12-04
## pandoc 3.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/tools/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
## abind 1.4-8 2024-09-12 [1] CRAN (R 4.3.0)
## AnnotationDbi * 1.64.1 2023-11-03 [1] Bioconductor
## aod 1.3.3 2023-12-13 [1] CRAN (R 4.3.0)
## ape 5.8 2024-04-11 [1] CRAN (R 4.3.0)
## aplot 0.2.3 2024-06-17 [1] CRAN (R 4.3.0)
## backports 1.5.0 2024-05-23 [1] CRAN (R 4.3.0)
## Biobase * 2.62.0 2023-10-24 [1] Bioconductor
## BiocGenerics * 0.48.1 2023-11-01 [1] Bioconductor
## BiocParallel * 1.36.0 2023-10-24 [1] Bioconductor
## Biostrings 2.70.3 2024-03-13 [1] Bioconductor 3.18 (R 4.3.0)
## bit 4.5.0 2024-09-20 [1] CRAN (R 4.3.0)
## bit64 4.5.2 2024-09-22 [1] CRAN (R 4.3.0)
## bitops 1.0-9 2024-10-03 [1] CRAN (R 4.3.0)
## blob 1.2.4 2023-03-17 [1] CRAN (R 4.3.0)
## bookdown 0.41 2024-10-16 [1] CRAN (R 4.3.0)
## boot 1.3-31 2024-08-28 [1] CRAN (R 4.3.0)
## broom 1.0.7 2024-09-26 [1] CRAN (R 4.3.0)
## bslib 0.8.0 2024-07-29 [1] CRAN (R 4.3.0)
## cachem 1.1.0 2024-05-16 [1] CRAN (R 4.3.0)
## caTools 1.18.3 2024-09-04 [1] CRAN (R 4.3.0)
## cli 3.6.3 2024-06-21 [1] CRAN (R 4.3.0)
## clusterProfiler * 4.10.1 2024-03-08 [1] Bioconductor 3.18 (R 4.3.0)
## codetools 0.2-20 2024-03-31 [1] CRAN (R 4.3.0)
## colorspace 2.1-1 2024-07-26 [1] CRAN (R 4.3.0)
## corpcor 1.6.10 2021-09-16 [1] CRAN (R 4.3.0)
## cowplot 1.1.3 2024-01-22 [1] CRAN (R 4.3.0)
## crayon 1.5.3 2024-06-20 [1] CRAN (R 4.3.0)
## data.table 1.16.2 2024-10-10 [1] CRAN (R 4.3.0)
## DBI 1.2.3 2024-06-02 [1] CRAN (R 4.3.0)
## DelayedArray 0.28.0 2023-10-24 [1] Bioconductor
## DESeq2 * 1.42.1 2024-03-06 [1] Bioconductor 3.18 (R 4.3.0)
## digest 0.6.37 2024-08-19 [1] CRAN (R 4.3.0)
## doParallel * 1.0.17 2022-02-07 [1] CRAN (R 4.3.0)
## DOSE 3.28.2 2023-12-10 [1] Bioconductor 3.18 (R 4.3.0)
## dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.3.0)
## edgeR * 4.0.16 2024-02-18 [1] Bioconductor 3.18 (R 4.3.0)
## enrichplot * 1.22.0 2023-10-24 [1] Bioconductor
## EnvStats 3.0.0 2024-08-24 [1] CRAN (R 4.3.0)
## evaluate 1.0.1 2024-10-10 [1] CRAN (R 4.3.0)
## fANCOVA 0.6-1 2020-11-13 [1] CRAN (R 4.3.0)
## fansi 1.0.6 2023-12-08 [1] CRAN (R 4.3.0)
## farver 2.1.2 2024-05-13 [1] CRAN (R 4.3.0)
## fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.3.0)
## fastmatch 1.1-4 2023-08-18 [1] CRAN (R 4.3.0)
## fgsea * 1.28.0 2023-10-24 [1] Bioconductor
## forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.3.0)
## foreach * 1.5.2 2022-02-02 [1] CRAN (R 4.3.0)
## fs 1.6.5 2024-10-30 [1] CRAN (R 4.3.0)
## generics 0.1.3 2022-07-05 [1] CRAN (R 4.3.0)
## GenomeInfoDb * 1.38.8 2024-03-15 [1] Bioconductor 3.18 (R 4.3.0)
## GenomeInfoDbData 1.2.11 2023-11-20 [1] Bioconductor
## GenomicRanges * 1.54.1 2023-10-29 [1] Bioconductor
## ggforce 0.4.2 2024-02-19 [1] CRAN (R 4.3.0)
## ggfun 0.1.7 2024-10-24 [1] CRAN (R 4.3.0)
## ggh4x * 0.2.8 2024-01-23 [1] CRAN (R 4.3.0)
## ggplot2 * 3.5.1 2024-04-23 [1] CRAN (R 4.3.0)
## ggplotify 0.1.2 2023-08-09 [1] CRAN (R 4.3.0)
## ggraph 2.2.1 2024-03-07 [1] CRAN (R 4.3.0)
## ggrepel 0.9.6 2024-09-07 [1] CRAN (R 4.3.0)
## ggtree 3.10.1 2024-02-25 [1] Bioconductor 3.18 (R 4.3.0)
## glue 1.8.0 2024-09-30 [1] CRAN (R 4.3.0)
## GO.db 3.18.0 2023-11-23 [1] Bioconductor
## GOSemSim 2.28.1 2024-01-17 [1] Bioconductor 3.18 (R 4.3.0)
## gplots 3.2.0 2024-10-05 [1] CRAN (R 4.3.0)
## graphlayouts 1.2.0 2024-09-24 [1] CRAN (R 4.3.0)
## gridExtra 2.3 2017-09-09 [1] CRAN (R 4.3.0)
## gridGraphics 0.5-1 2020-12-13 [1] CRAN (R 4.3.0)
## grpSciRmdTheme * 0.4.0.1 2023-12-03 [1] Github (guillermo1996/grpSciRmdTheme@5f7645e)
## gson 0.1.0 2023-03-07 [1] CRAN (R 4.3.0)
## gtable 0.3.6 2024-10-25 [1] CRAN (R 4.3.0)
## gtools 3.9.5 2023-11-20 [1] CRAN (R 4.3.0)
## HDO.db 0.99.1 2023-11-23 [1] Bioconductor
## hms 1.1.3 2023-03-21 [1] CRAN (R 4.3.0)
## htmltools 0.5.8.1 2024-04-04 [1] CRAN (R 4.3.0)
## httr 1.4.7 2023-08-15 [1] CRAN (R 4.3.0)
## igraph 2.1.1 2024-10-19 [1] CRAN (R 4.3.0)
## IRanges * 2.36.0 2023-10-24 [1] Bioconductor
## iterators * 1.0.14 2022-02-05 [1] CRAN (R 4.3.0)
## jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.3.0)
## jsonlite 1.8.9 2024-09-20 [1] CRAN (R 4.3.0)
## KEGGREST 1.42.0 2023-10-24 [1] Bioconductor
## KernSmooth 2.23-24 2024-05-17 [1] CRAN (R 4.3.0)
## knitr 1.49 2024-11-08 [1] CRAN (R 4.3.0)
## lattice 0.22-6 2024-03-20 [1] CRAN (R 4.3.0)
## lazyeval 0.2.2 2019-03-15 [1] CRAN (R 4.3.0)
## lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.3.0)
## limma * 3.58.1 2023-10-31 [1] Bioconductor
## lme4 1.1-35.5 2024-07-03 [1] CRAN (R 4.3.0)
## lmerTest 3.1-3 2020-10-23 [1] CRAN (R 4.3.0)
## locfit 1.5-9.10 2024-06-24 [1] CRAN (R 4.3.0)
## lubridate * 1.9.3 2023-09-27 [1] CRAN (R 4.3.0)
## magrittr * 2.0.3 2022-03-30 [1] CRAN (R 4.3.0)
## MASS 7.3-60.0.1 2024-01-13 [1] CRAN (R 4.3.0)
## Matrix 1.6-5 2024-01-11 [1] CRAN (R 4.3.0)
## MatrixGenerics * 1.14.0 2023-10-24 [1] Bioconductor
## matrixStats * 1.4.1 2024-09-08 [1] CRAN (R 4.3.0)
## memoise 2.0.1 2021-11-26 [1] CRAN (R 4.3.0)
## minqa 1.2.8 2024-08-17 [1] CRAN (R 4.3.0)
## munsell 0.5.1 2024-04-01 [1] CRAN (R 4.3.0)
## mvtnorm 1.3-2 2024-11-04 [1] CRAN (R 4.3.0)
## nlme 3.1-166 2024-08-14 [1] CRAN (R 4.3.0)
## nloptr 2.1.1 2024-06-25 [1] CRAN (R 4.3.0)
## numDeriv 2016.8-1.1 2019-06-06 [1] CRAN (R 4.3.0)
## org.Hs.eg.db * 3.18.0 2023-11-23 [1] Bioconductor
## patchwork * 1.3.0 2024-09-16 [1] CRAN (R 4.3.0)
## pbkrtest 0.5.3 2024-06-26 [1] CRAN (R 4.3.0)
## pillar 1.9.0 2023-03-22 [1] CRAN (R 4.3.0)
## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.3.0)
## plyr 1.8.9 2023-10-02 [1] CRAN (R 4.3.0)
## png 0.1-8 2022-11-29 [1] CRAN (R 4.3.0)
## polyclip 1.10-7 2024-07-23 [1] CRAN (R 4.3.0)
## purrr * 1.0.2 2023-08-10 [1] CRAN (R 4.3.0)
## qvalue 2.34.0 2023-10-24 [1] Bioconductor
## R6 2.5.1 2021-08-19 [1] CRAN (R 4.3.0)
## rbibutils 2.3 2024-10-04 [1] CRAN (R 4.3.0)
## RColorBrewer 1.1-3 2022-04-03 [1] CRAN (R 4.3.0)
## Rcpp 1.0.13-1 2024-11-02 [1] CRAN (R 4.3.0)
## RCurl 1.98-1.16 2024-07-11 [1] CRAN (R 4.3.0)
## Rdpack 2.6.1 2024-08-06 [1] CRAN (R 4.3.0)
## readr * 2.1.5 2024-01-10 [1] CRAN (R 4.3.0)
## remaCor 0.0.18 2024-02-08 [1] CRAN (R 4.3.0)
## reshape2 1.4.4 2020-04-09 [1] CRAN (R 4.3.0)
## RhpcBLASctl 0.23-42 2023-02-11 [1] CRAN (R 4.3.0)
## rlang 1.1.4 2024-06-04 [1] CRAN (R 4.3.0)
## rmarkdown 2.29 2024-11-04 [1] CRAN (R 4.3.0)
## RSQLite 2.3.7 2024-05-27 [1] CRAN (R 4.3.0)
## rstudioapi 0.17.1 2024-10-22 [1] CRAN (R 4.3.0)
## S4Arrays 1.2.1 2024-03-04 [1] Bioconductor 3.18 (R 4.3.0)
## S4Vectors * 0.40.2 2023-11-23 [1] Bioconductor 3.18 (R 4.3.0)
## sass 0.4.9 2024-03-15 [1] CRAN (R 4.3.0)
## scales * 1.3.0 2023-11-28 [1] CRAN (R 4.3.0)
## scatterpie 0.2.4 2024-08-28 [1] CRAN (R 4.3.0)
## sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.3.0)
## shadowtext 0.1.4 2024-07-18 [1] CRAN (R 4.3.0)
## SparseArray 1.2.4 2024-02-11 [1] Bioconductor 3.18 (R 4.3.0)
## statmod 1.5.0 2023-01-06 [1] CRAN (R 4.3.0)
## stringi 1.8.4 2024-05-06 [1] CRAN (R 4.3.0)
## stringr * 1.5.1 2023-11-14 [1] CRAN (R 4.3.0)
## SummarizedExperiment * 1.32.0 2023-10-24 [1] Bioconductor
## tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.3.0)
## tidygraph 1.3.1 2024-01-30 [1] CRAN (R 4.3.0)
## tidyr * 1.3.1 2024-01-24 [1] CRAN (R 4.3.0)
## tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.3.0)
## tidytree 0.4.6 2023-12-12 [1] CRAN (R 4.3.0)
## tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.3.0)
## timechange 0.3.0 2024-01-18 [1] CRAN (R 4.3.0)
## treeio 1.26.0 2023-10-24 [1] Bioconductor
## tweenr 2.0.3 2024-02-26 [1] CRAN (R 4.3.0)
## tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.3.0)
## utf8 1.2.4 2023-10-22 [1] CRAN (R 4.3.0)
## variancePartition * 1.32.5 2024-02-16 [1] Bioconductor 3.18 (R 4.3.0)
## vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.3.0)
## viridis 0.6.5 2024-01-29 [1] CRAN (R 4.3.0)
## viridisLite 0.4.2 2023-05-02 [1] CRAN (R 4.3.0)
## vroom 1.6.5 2023-12-05 [1] CRAN (R 4.3.0)
## withr 3.0.2 2024-10-28 [1] CRAN (R 4.3.0)
## xfun 0.49 2024-10-31 [1] CRAN (R 4.3.0)
## XVector 0.42.0 2023-10-24 [1] Bioconductor
## yaml 2.3.10 2024-07-26 [1] CRAN (R 4.3.0)
## yulab.utils 0.1.8 2024-11-07 [1] CRAN (R 4.3.0)
## zlibbioc 1.48.2 2024-03-13 [1] Bioconductor 3.18 (R 4.3.0)
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## [1] /opt/R/4.3.0/lib/R/library
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